Pub Date : 2023-06-24DOI: 10.37385/jaets.v4i2.2163
Lavanya Yamathi, K. Rani, P. Krishna
with the help of effective DL(Deep Learning) based algorithms. Though several clinical procedures and imaging modalities exists to diagnose Covid-19, these methods are time-consuming processes and sometimes the predictions are incorrect. Concurrently, AI (Artificial Intelligence) based DL models have gained attention in this area due to its innate capability for efficient learning. Though conventional systems have tried to perform better prediction, they lacked in accuracy with prediction rate. Moreover, the conventional systems have not utilized attention model completely for Covid-19 detection. This research is intended to resolve these pitfalls of covid-19 detection methods with the help of deep feature wise attention based Convolutional Neural Network. For this purpose, the data has been pre-processed by image resizing, the Residual Descriptor with Conv-BAM(Convolutional Block Attention Module) has been employed to obtain refined features from spatial and channel wise attention based module. The obtained features are used in the present study to improvise the classification as covid positive or negative. The performance of the proposed system has been assessed with regard to metrics to prove better efficiency. The proposed method achieved high accuracy rate of 97.82%. This DL based model can be used as a supplementary tool in the diagnosis of Covid-19 alongside other diagnostic method
{"title":"Deep Feature Wise Attention Based Convolutional Neural Network for Covid-19 Detection Using Lung CT Scan Images","authors":"Lavanya Yamathi, K. Rani, P. Krishna","doi":"10.37385/jaets.v4i2.2163","DOIUrl":"https://doi.org/10.37385/jaets.v4i2.2163","url":null,"abstract":"with the help of effective DL(Deep Learning) based algorithms. Though several clinical procedures and imaging modalities exists to diagnose Covid-19, these methods are time-consuming processes and sometimes the predictions are incorrect. Concurrently, AI (Artificial Intelligence) based DL models have gained attention in this area due to its innate capability for efficient learning. Though conventional systems have tried to perform better prediction, they lacked in accuracy with prediction rate. Moreover, the conventional systems have not utilized attention model completely for Covid-19 detection. This research is intended to resolve these pitfalls of covid-19 detection methods with the help of deep feature wise attention based Convolutional Neural Network. For this purpose, the data has been pre-processed by image resizing, the Residual Descriptor with Conv-BAM(Convolutional Block Attention Module) has been employed to obtain refined features from spatial and channel wise attention based module. The obtained features are used in the present study to improvise the classification as covid positive or negative. The performance of the proposed system has been assessed with regard to metrics to prove better efficiency. The proposed method achieved high accuracy rate of 97.82%. This DL based model can be used as a supplementary tool in the diagnosis of Covid-19 alongside other diagnostic method","PeriodicalId":34350,"journal":{"name":"Journal of Applied Engineering and Technological Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48485955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-24DOI: 10.37385/jaets.v4i2.2164
R. Yamuna, Rajani Rajalingam, M. Rani
The increasing prevalence of brain tumors necessitates accurate and efficient methods for their identification and classification. While deep learning (DL) models have shown promise in this domain, their computational demands pose challenges when deploying them on resource-constrained mobile devices. This paper investigates the potential of Mobile Edge Computing (MEC) and Task Offloading to improve the performance of DL models for brain tumor classification. A comprehensive framework was developed, considering the computational capabilities of mobile devices and edge servers, as well as communication costs associated with task offloading. Various factors influencing task offloading decisions were analyzed, including model size, available resources, and network conditions. Results demonstrate that task offloading effectively reduces the time and energy required to process DL models for brain tumor classification, while maintaining accuracy. The study emphasizes the need to balance computation and communication costs when deciding on task offloading. These findings have significant implications for the development of efficient mobile edge computing systems for medical applications. Leveraging MEC and Task Offloading enables healthcare professionals to utilize DL models for brain tumor classification on resource-constrained mobile devices, ensuring accurate and timely diagnoses. These technological advancements pave the way for more accessible and efficient medical solutions in the future.
{"title":"Performance Analysis of Task Offloading in Mobile Edge Cloud Computing for Brain Tumor Classification Using Deep Learning","authors":"R. Yamuna, Rajani Rajalingam, M. Rani","doi":"10.37385/jaets.v4i2.2164","DOIUrl":"https://doi.org/10.37385/jaets.v4i2.2164","url":null,"abstract":"The increasing prevalence of brain tumors necessitates accurate and efficient methods for their identification and classification. While deep learning (DL) models have shown promise in this domain, their computational demands pose challenges when deploying them on resource-constrained mobile devices. This paper investigates the potential of Mobile Edge Computing (MEC) and Task Offloading to improve the performance of DL models for brain tumor classification. A comprehensive framework was developed, considering the computational capabilities of mobile devices and edge servers, as well as communication costs associated with task offloading. Various factors influencing task offloading decisions were analyzed, including model size, available resources, and network conditions. Results demonstrate that task offloading effectively reduces the time and energy required to process DL models for brain tumor classification, while maintaining accuracy. The study emphasizes the need to balance computation and communication costs when deciding on task offloading. These findings have significant implications for the development of efficient mobile edge computing systems for medical applications. Leveraging MEC and Task Offloading enables healthcare professionals to utilize DL models for brain tumor classification on resource-constrained mobile devices, ensuring accurate and timely diagnoses. These technological advancements pave the way for more accessible and efficient medical solutions in the future.","PeriodicalId":34350,"journal":{"name":"Journal of Applied Engineering and Technological Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41806557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-20DOI: 10.37385/jaets.v4i2.1394
Anthony Ejeh Itodo, T. Swart
Several efforts are being made to improve the capacity of 5G networks using emerging technologies of interest. One of the indispensable technologies to fulfill the need is device-to-device (D2D) communication with its untapped associated benefits. Interference is introduced at the base station due to massive traffic congestion. The purpose of this research is to expand the knowledge of interference mitigation in D2D using stochastic geometrical tools which will result in capacity enhancement. This study uses a literature review method based on 5G and other already existing literature on D2D communication. More than one hundred and twenty papers on D2D communications in cellular networks exist but no precise survey paper on interference management to enhance capacity using stochastic geometrical tools exists. The contribution of this survey to theory is that apart from already existing capacity enhancement methods, interference mitigation using stochastic geometrical tools is another technique that can also be used for capacity enhancement in D2D communications.
{"title":"Capacity Enhancement in D2D 5G Emerging Networks: A Survey","authors":"Anthony Ejeh Itodo, T. Swart","doi":"10.37385/jaets.v4i2.1394","DOIUrl":"https://doi.org/10.37385/jaets.v4i2.1394","url":null,"abstract":"Several efforts are being made to improve the capacity of 5G networks using emerging technologies of interest. One of the indispensable technologies to fulfill the need is device-to-device (D2D) communication with its untapped associated benefits. Interference is introduced at the base station due to massive traffic congestion. The purpose of this research is to expand the knowledge of interference mitigation in D2D using stochastic geometrical tools which will result in capacity enhancement. This study uses a literature review method based on 5G and other already existing literature on D2D communication. More than one hundred and twenty papers on D2D communications in cellular networks exist but no precise survey paper on interference management to enhance capacity using stochastic geometrical tools exists. The contribution of this survey to theory is that apart from already existing capacity enhancement methods, interference mitigation using stochastic geometrical tools is another technique that can also be used for capacity enhancement in D2D communications.","PeriodicalId":34350,"journal":{"name":"Journal of Applied Engineering and Technological Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48193431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-12DOI: 10.37385/jaets.v4i2.1964
Ahmad Zamsuri, Sarjon Defit, G. W. Nurcahyo
Emotions are expressions manifested by individuals in response to what they see or experience. In this study, emotions were examined through individuals' tweets regarding the election issues in Indonesia in 2024. The collected tweets were then labeled based on emotions using the emotion wheel, which consisted of six categories: joy, love, surprise, anger, fear, and sadness. After the labeling process, the next step involved weighting using TF-IDF (Term Frequency-Inverse Document Frequency) and Bag-of-Words (BoW) techniques. Subsequently, the model was evaluated using the K-Nearest Neighbor (KNN) algorithm with three different data splitting ratios: 80:20, 70:30, and 60:40. From the six labels used in the modeling process, the accuracy was then calculated, and the labels were subsequently merged into positive and negative categories. Then the modeling was conducted using the same process with the six labels. The results of this study revealed that the utilization of TF-IDF outperformed BoW. The highest accuracy was achieved with the 80:20 data splitting ratio, attaining 58% accuracy for the six-label classification and 79% accuracy for the two-label classification
{"title":"Classification of Multiple Emotions in Indonesian Text Using The K-Nearest Neighbor Method","authors":"Ahmad Zamsuri, Sarjon Defit, G. W. Nurcahyo","doi":"10.37385/jaets.v4i2.1964","DOIUrl":"https://doi.org/10.37385/jaets.v4i2.1964","url":null,"abstract":"Emotions are expressions manifested by individuals in response to what they see or experience. In this study, emotions were examined through individuals' tweets regarding the election issues in Indonesia in 2024. The collected tweets were then labeled based on emotions using the emotion wheel, which consisted of six categories: joy, love, surprise, anger, fear, and sadness. After the labeling process, the next step involved weighting using TF-IDF (Term Frequency-Inverse Document Frequency) and Bag-of-Words (BoW) techniques. Subsequently, the model was evaluated using the K-Nearest Neighbor (KNN) algorithm with three different data splitting ratios: 80:20, 70:30, and 60:40. From the six labels used in the modeling process, the accuracy was then calculated, and the labels were subsequently merged into positive and negative categories. Then the modeling was conducted using the same process with the six labels. The results of this study revealed that the utilization of TF-IDF outperformed BoW. The highest accuracy was achieved with the 80:20 data splitting ratio, attaining 58% accuracy for the six-label classification and 79% accuracy for the two-label classification","PeriodicalId":34350,"journal":{"name":"Journal of Applied Engineering and Technological Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45729403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-09DOI: 10.37385/jaets.v4i2.2013
Bhasha Pydala, T. P. Kumar, K. Baseer
Vision is extremely important in our lives. The loss of sight is a serious issue for anyone. According to the WHO, one-sixth of the world's population suffers from vision impairment. According to World Health Organization (WHO) statistics published in December 2021, more than 283 million people worldwide suffer from sight problems, including 39 million blind people and 228 million people with low vision. Navigation in unfamiliar environments is a significant challenge for the partially sighted and visually impaired. Improving visual information on object location and content can aid navigation in unfamiliar environments. Many efforts have been made over the years to develop various devices to assist the visually impaired and improve their quality of life. Numerous efforts have been made over the decades to develop gadgets to support the visually impaired as well as enhance the quality of their lives by trying to make them skilled. There are many existing navigation alternatives that can aid these people. However, in practice, navigation alternatives are infrequently adopted and implemented. For universal use, many of these gadgets are either too heavy or too expensive. While emphasizing related strengths and limitations, it is necessary to produce a minimally expensive assistive device for people with visual disabilities. The proposed model provides an efficient solution for VIPs to roam from place to place by themselves through smart applications with AI and sensor technology. The smart application captures and classifies the images. The obstacles are detected through ultrasonic sensors. The user can get a sense of the obstacles in the path through voice command. The proposed model is very helpful for the VIPs in terms of qualitative and quantitative performance measures. This enables a ranking of the evaluated systems according to their potential influence on Visually Impaired people's lives.
{"title":"Smart_Eye: A Navigation and Obstacle Detection for Visually Impaired People through Smart App","authors":"Bhasha Pydala, T. P. Kumar, K. Baseer","doi":"10.37385/jaets.v4i2.2013","DOIUrl":"https://doi.org/10.37385/jaets.v4i2.2013","url":null,"abstract":"Vision is extremely important in our lives. The loss of sight is a serious issue for anyone. According to the WHO, one-sixth of the world's population suffers from vision impairment. According to World Health Organization (WHO) statistics published in December 2021, more than 283 million people worldwide suffer from sight problems, including 39 million blind people and 228 million people with low vision. Navigation in unfamiliar environments is a significant challenge for the partially sighted and visually impaired. Improving visual information on object location and content can aid navigation in unfamiliar environments. Many efforts have been made over the years to develop various devices to assist the visually impaired and improve their quality of life. Numerous efforts have been made over the decades to develop gadgets to support the visually impaired as well as enhance the quality of their lives by trying to make them skilled. There are many existing navigation alternatives that can aid these people. However, in practice, navigation alternatives are infrequently adopted and implemented. For universal use, many of these gadgets are either too heavy or too expensive. While emphasizing related strengths and limitations, it is necessary to produce a minimally expensive assistive device for people with visual disabilities. The proposed model provides an efficient solution for VIPs to roam from place to place by themselves through smart applications with AI and sensor technology. The smart application captures and classifies the images. The obstacles are detected through ultrasonic sensors. The user can get a sense of the obstacles in the path through voice command. The proposed model is very helpful for the VIPs in terms of qualitative and quantitative performance measures. This enables a ranking of the evaluated systems according to their potential influence on Visually Impaired people's lives. \u0000 ","PeriodicalId":34350,"journal":{"name":"Journal of Applied Engineering and Technological Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44803822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-05DOI: 10.37385/jaets.v4i2.1989
Tengku Nurainun, H. A. Talib, K. R. Jamaludin, S. Yusof, N. T. Putri, F. Lestari
The need to apply halal management practices to non-food industries today is still merely seen as a necessity to meet the requirements of Islamic rules. Meanwhile, this approach has demonstrated that it can improve organizational efficacy in a variety of contexts. This study seeks to investigate the depth of halal principles implementation among leather industries and comes up with strategies for how small and medium-sized enterprises (SMEs) in the leather industry can use halal management practices to move toward halal certification and enhance its performance. An exploratory-descriptive approach was used to get the current state of halal practices among leather industry SMEs through interviews and survey questionnaires. Five stakeholders were interviewed in a semi-structured manner. A survey questionnaire was distributed to 127 SMEs in the leather industry center of Sukaregang, Garut, Indonesia. This paper discusses the key factors of halal implementation and determines which halal practices need more emphasis. The result showed that the current knowledge, awareness, and implementation of halal requirements among leather SMEs in Indonesia are still low. An action plan for the industry, authority, and supplier was provided. The implication of this research can contribute to the leather industry players that intent to implement halal management system effectively and stakeholders in making decision to accelerate halal certification process.
{"title":"An Exploratory-Descriptive Review of The Potential For Halal Management Implementation in Indonesian Leather Businesses","authors":"Tengku Nurainun, H. A. Talib, K. R. Jamaludin, S. Yusof, N. T. Putri, F. Lestari","doi":"10.37385/jaets.v4i2.1989","DOIUrl":"https://doi.org/10.37385/jaets.v4i2.1989","url":null,"abstract":"The need to apply halal management practices to non-food industries today is still merely seen as a necessity to meet the requirements of Islamic rules. Meanwhile, this approach has demonstrated that it can improve organizational efficacy in a variety of contexts. This study seeks to investigate the depth of halal principles implementation among leather industries and comes up with strategies for how small and medium-sized enterprises (SMEs) in the leather industry can use halal management practices to move toward halal certification and enhance its performance. An exploratory-descriptive approach was used to get the current state of halal practices among leather industry SMEs through interviews and survey questionnaires. Five stakeholders were interviewed in a semi-structured manner. A survey questionnaire was distributed to 127 SMEs in the leather industry center of Sukaregang, Garut, Indonesia. This paper discusses the key factors of halal implementation and determines which halal practices need more emphasis. The result showed that the current knowledge, awareness, and implementation of halal requirements among leather SMEs in Indonesia are still low. An action plan for the industry, authority, and supplier was provided. The implication of this research can contribute to the leather industry players that intent to implement halal management system effectively and stakeholders in making decision to accelerate halal certification process.","PeriodicalId":34350,"journal":{"name":"Journal of Applied Engineering and Technological Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45807252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-05DOI: 10.37385/jaets.v4i2.1873
M. Q. Huda, N. Hidayah, N. A. Zakaria, Eva Khudzaeva
Implementing IT innovation in organizations is a complex and challenging process that affects organizational problems. The process involves many interacting factors and actors; hence this situation is difficult to control. This problem demonstrates the need to understand researchers' perceptions of IT adoption and implementation. This study aims to explore in depth the adoption and implementation of self-development IT applications (SDIT) in Islamic-based Higher Education Institution (IHEI) in Indonesia. The IT Adoption and Implementation Framework (Irawan et al., 2018) was applied as a lens to investigate the case. We conducted in-depth interviews with key informants involved during the adoption and implementation process in the organization. Interviews were transcribed and analyzed using thematic analysis. Certain Focus Group Discussion (FGD) studies and specific interviews with key informants representing three levels of management explained that mediating factors such as post-implementation interventions, subjective norms, and facilitating conditions influence success in adopting and implementing IT innovations in such cases. This study concludes that managerial interventions play an important role in reducing resistance from authoritarian approaches to mandating use and serve as a determinant of its sustainability in the future. These findings have significant implications for understanding how to achieve success in IT adoption and implementation in the post-implementation phase by providing empirical evidence. Theoretically, this study contributes to IT adoption and implementation frameworks by identifying the active role of critical actors in the adoption and implementation of IT applications in higher education institutions.
{"title":"Adoption and Implementation of Self-Development IT Applications : An Empirical Study of State Islamic Higher Education Institutions in Indonesia","authors":"M. Q. Huda, N. Hidayah, N. A. Zakaria, Eva Khudzaeva","doi":"10.37385/jaets.v4i2.1873","DOIUrl":"https://doi.org/10.37385/jaets.v4i2.1873","url":null,"abstract":"Implementing IT innovation in organizations is a complex and challenging process that affects organizational problems. The process involves many interacting factors and actors; hence this situation is difficult to control. This problem demonstrates the need to understand researchers' perceptions of IT adoption and implementation. This study aims to explore in depth the adoption and implementation of self-development IT applications (SDIT) in Islamic-based Higher Education Institution (IHEI) in Indonesia. The IT Adoption and Implementation Framework (Irawan et al., 2018) was applied as a lens to investigate the case. We conducted in-depth interviews with key informants involved during the adoption and implementation process in the organization. Interviews were transcribed and analyzed using thematic analysis. Certain Focus Group Discussion (FGD) studies and specific interviews with key informants representing three levels of management explained that mediating factors such as post-implementation interventions, subjective norms, and facilitating conditions influence success in adopting and implementing IT innovations in such cases. This study concludes that managerial interventions play an important role in reducing resistance from authoritarian approaches to mandating use and serve as a determinant of its sustainability in the future. These findings have significant implications for understanding how to achieve success in IT adoption and implementation in the post-implementation phase by providing empirical evidence. Theoretically, this study contributes to IT adoption and implementation frameworks by identifying the active role of critical actors in the adoption and implementation of IT applications in higher education institutions.","PeriodicalId":34350,"journal":{"name":"Journal of Applied Engineering and Technological Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43674631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-05DOI: 10.37385/jaets.v4i2.1925
Dersin Daimari, Subhash Mondal, Bihung Brahma, Amitava Nag
Recent years have seen the rapid deployment of Artificial Intelligence (AI) which allows systems to take intelligent decisions. AI breakthroughs could radically change modern libraries' operations. However, introducing AI in modern libraries is a challenging task. This research explores the potential for smart libraries to improve the caliber of user services through the use of machine learning (ML) techniques. The proposed work investigates machine learning methods such as Random Forest (RF) and boosting algorithms, including Light Gradient Boosting Machine (LGBM), Histogram-based gradient boosting (HGB), Extreme gradient boosting (XGB), CatBoost (CB), AdaBoost (AB), and Gradient Boosting (GB) for the task of identifying and classifying Favorite books and compares their performances. Comprehensive experiments performed on the publicly available dataset (Art Garfunkel's Library) show that the proposed model can effectively handle the task of identifying and classifying Favorite books. Experimental results show that LGBM has achieved outstanding performance with an accuracy rate of 94.9367% than Random Forest and other boosting ML algorithms. This empirical research work takes advantage of AI adoption in libraries using machine learning techniques. To the best of our knowledge, we are the first to develop an intelligent application for the modern library to automatically identify and classify Favorite books
{"title":"Favorite Book Prediction System Using Machine Learning Algorithms","authors":"Dersin Daimari, Subhash Mondal, Bihung Brahma, Amitava Nag","doi":"10.37385/jaets.v4i2.1925","DOIUrl":"https://doi.org/10.37385/jaets.v4i2.1925","url":null,"abstract":"Recent years have seen the rapid deployment of Artificial Intelligence (AI) which allows systems to take intelligent decisions. AI breakthroughs could radically change modern libraries' operations. However, introducing AI in modern libraries is a challenging task. This research explores the potential for smart libraries to improve the caliber of user services through the use of machine learning (ML) techniques. The proposed work investigates machine learning methods such as Random Forest (RF) and boosting algorithms, including Light Gradient Boosting Machine (LGBM), Histogram-based gradient boosting (HGB), Extreme gradient boosting (XGB), CatBoost (CB), AdaBoost (AB), and Gradient Boosting (GB) for the task of identifying and classifying Favorite books and compares their performances. Comprehensive experiments performed on the publicly available dataset (Art Garfunkel's Library) show that the proposed model can effectively handle the task of identifying and classifying Favorite books. Experimental results show that LGBM has achieved outstanding performance with an accuracy rate of 94.9367% than Random Forest and other boosting ML algorithms. This empirical research work takes advantage of AI adoption in libraries using machine learning techniques. To the best of our knowledge, we are the first to develop an intelligent application for the modern library to automatically identify and classify Favorite books","PeriodicalId":34350,"journal":{"name":"Journal of Applied Engineering and Technological Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135703158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-05DOI: 10.37385/jaets.v4i2.1646
Taufik Hidayat, K. Ramli, R. D. Mardian, Rahutomo Mahardiko
The industry and government have recently acknowledged and used virtual machines (VM) to promote their businesses. During the process of VM, some problems might occur. The issues, such as a heavy load of memory, a large load of CPU, a massive load of a disk, a high load of network and time-defined migration, might interrupt the business processes. This paper identifies the migration process among hosts for VM to overcome the problem within the defined time frame of migration. The introduction of VMs migration in a timely manner is to detect a problem earlier. There are workload parameters, such as network, CPU, disk and memory as our parameters. To overcome the issue, we have to follow the Model named Fuzzy rule. The rule follows the basic of tree model for decision-making. The application of the fuzzy Model for the study is to determine VMs allocation from busy VMs to vacant VMs for balancing purposes. The result of the study showed that the use of the fuzzy Model to forecast VMs migration based on the defined rule had 2 positive impacts. The positive impacts are (1) Time frame live migration of VMs can reduce workload by 80 %. This aims to reduce failures in performing a live migration of VMs to increase data center performance. (2) In testing, the fuzzy Model can provide results with an accuracy of 90 %, so this model can perform a live migration of VMs precisely in determining the execution time. Next, the workload could be balanced among VMs. This research could be used further to improve 5G Quality of Experience (QoE) shortly.
{"title":"Towards Improving 5G Quality of Experience: Fuzzy as a Mathematical Model to Migrate Virtual Machine Server in The Defined Time Frame","authors":"Taufik Hidayat, K. Ramli, R. D. Mardian, Rahutomo Mahardiko","doi":"10.37385/jaets.v4i2.1646","DOIUrl":"https://doi.org/10.37385/jaets.v4i2.1646","url":null,"abstract":"The industry and government have recently acknowledged and used virtual machines (VM) to promote their businesses. During the process of VM, some problems might occur. The issues, such as a heavy load of memory, a large load of CPU, a massive load of a disk, a high load of network and time-defined migration, might interrupt the business processes. This paper identifies the migration process among hosts for VM to overcome the problem within the defined time frame of migration. The introduction of VMs migration in a timely manner is to detect a problem earlier. There are workload parameters, such as network, CPU, disk and memory as our parameters. To overcome the issue, we have to follow the Model named Fuzzy rule. The rule follows the basic of tree model for decision-making. The application of the fuzzy Model for the study is to determine VMs allocation from busy VMs to vacant VMs for balancing purposes. The result of the study showed that the use of the fuzzy Model to forecast VMs migration based on the defined rule had 2 positive impacts. The positive impacts are (1) Time frame live migration of VMs can reduce workload by 80 %. This aims to reduce failures in performing a live migration of VMs to increase data center performance. (2) In testing, the fuzzy Model can provide results with an accuracy of 90 %, so this model can perform a live migration of VMs precisely in determining the execution time. Next, the workload could be balanced among VMs. This research could be used further to improve 5G Quality of Experience (QoE) shortly.","PeriodicalId":34350,"journal":{"name":"Journal of Applied Engineering and Technological Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42394119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-05DOI: 10.37385/jaets.v4i2.1763
Afrizal Mansur, J. Jamaluddin, Jumni Nelli, Muhammad Hanif, N. Wahid, Haswir Haswir, Muhammad Marizal
The attack on the twin towers of the World Trade Center (WTC) on September 11, 2011 in New York, United States caused madrasas to be considered Islamic schools that gave birth to a radical generation. Madrasas' efforts to improve this negative image by improving the quality of education, especially 10 years after the incident, have succeeded in making Madrasas the schools of choice for students in Indonesia. This study focuses on analyzing the rise of Madrasah Tsanawiyah in the city of Pekanbaru, especially on students' mastery of knowledge for the last 4 years (2016, 2017, 2018 and 2019) by means of spatial analysis. The progress of science in Madrasas can be seen from the mapping of the value of knowledge, especially in the downtown area which is complete with various facilities and activity centers in Pekanbaru. The performance of some madrasas is almost the same as SMA in terms of mastery of knowledge. This study leads to an important analysis that the Pekanbaru madrasa as a Muslim-majority city has succeeded in making madrasas the main choice of parents to equip their children with religious and scientific education, which indirectly proves that madrasas do not provide space for the formation of radical Islamic generations, on the contrary. Madrasas have succeeded in forming a generation of Muslims who have good religious and scientific knowledge
{"title":"Application of Spatial Analysis to Eliminating Radicalism in Madrasah Schools","authors":"Afrizal Mansur, J. Jamaluddin, Jumni Nelli, Muhammad Hanif, N. Wahid, Haswir Haswir, Muhammad Marizal","doi":"10.37385/jaets.v4i2.1763","DOIUrl":"https://doi.org/10.37385/jaets.v4i2.1763","url":null,"abstract":"The attack on the twin towers of the World Trade Center (WTC) on September 11, 2011 in New York, United States caused madrasas to be considered Islamic schools that gave birth to a radical generation. Madrasas' efforts to improve this negative image by improving the quality of education, especially 10 years after the incident, have succeeded in making Madrasas the schools of choice for students in Indonesia. This study focuses on analyzing the rise of Madrasah Tsanawiyah in the city of Pekanbaru, especially on students' mastery of knowledge for the last 4 years (2016, 2017, 2018 and 2019) by means of spatial analysis. The progress of science in Madrasas can be seen from the mapping of the value of knowledge, especially in the downtown area which is complete with various facilities and activity centers in Pekanbaru. The performance of some madrasas is almost the same as SMA in terms of mastery of knowledge. This study leads to an important analysis that the Pekanbaru madrasa as a Muslim-majority city has succeeded in making madrasas the main choice of parents to equip their children with religious and scientific education, which indirectly proves that madrasas do not provide space for the formation of radical Islamic generations, on the contrary. Madrasas have succeeded in forming a generation of Muslims who have good religious and scientific knowledge","PeriodicalId":34350,"journal":{"name":"Journal of Applied Engineering and Technological Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48212193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：